Integrated root phenotypes for improved rice performance under low nitrogen availability.
Integrated root phenotypes for improved rice performance under low nitrogen availability.
复制标题
低氮条件下改善水稻生产性能的综合根系表型。
DOI:
10.1111/pce.14284
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发表时间:
2022-03
影响因子:
7.3
通讯作者:
Lynch, Jonathan P.
中科院分区:
文献类型:
--
作者:
Ajmera, Ishan;Henry, Amelia;Radanielson, Ando M.;Klein, Stephanie P.;Ianevski, Aleksandr;Bennett, Malcolm J.;Band, Leah R.;Lynch, Jonathan P.
关键词:
Greater nitrogen efficiency would substantially reduce the economic, energy and environmental costs of rice production. We hypothesized that synergistic balancing of the costs and benefits for soil exploration among root architectural phenes is beneficial under suboptimal nitrogen availability. An enhanced implementation of the functional–structural model OpenSimRoot for rice integrated with the ORYZA_v3 crop model was used to evaluate the utility of combinations of root architectural phenes, namely nodal root angle, the proportion of smaller diameter nodal roots, nodal root number; and L‐type and S‐type lateral branching densities, for plant growth under low nitrogen. Multiple integrated root phenotypes were identified with greater shoot biomass under low nitrogen than the reference cultivar IR64. The superiority of these phenotypes was due to synergism among root phenes rather than the expected additive effects of phene states. Representative optimal phenotypes were predicted to have up to 80% greater grain yield with low N supply in the rainfed dry direct‐seeded agroecosystem over future weather conditions, compared to IR64. These phenotypes merit consideration as root ideotypes for breeding rice cultivars with improved yield under rainfed dry direct‐seeded conditions with limited nitrogen availability. The importance of phene synergism for the performance of integrated phenotypes has implications for crop breeding. Multiscale mechanistic modelling identified several integrated root phenotypes in rice with superior yield under low N availability. Synergism among root phenes was an important component of phenotypic performance.
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影响因子:
4.9
作者:
Confalonieri, Roberto;Bregaglio, Simone;Bouman, Bas
通讯作者:
Bouman, Bas
影响因子:
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影响因子:
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通讯作者:
PENG, S
影响因子:
6.6
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Bouman, BAM;van Laar, HH
通讯作者:
van Laar, HH
影响因子:
6.9
作者:
Chimungu JG;Loades KW;Lynch JP
通讯作者:
Lynch JP